CN113705585A - 基于神经网络实现的方法和系统 - Google Patents

基于神经网络实现的方法和系统 Download PDF

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CN113705585A
CN113705585A CN202111113164.1A CN202111113164A CN113705585A CN 113705585 A CN113705585 A CN 113705585A CN 202111113164 A CN202111113164 A CN 202111113164A CN 113705585 A CN113705585 A CN 113705585A
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pair
amino acid
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H·高
K-H·法尔
S·雷迪帕迪格帕蒂
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Inmair Ltd
Illumina Inc
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Priority claimed from US16/407,149 external-priority patent/US10540591B2/en
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    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06N3/02Neural networks
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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/24133Distances to prototypes
    • GPHYSICS
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/44Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30Detection of binding sites or motifs
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10Sequence alignment; Homology search
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20Supervised data analysis
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16B50/00ICT programming tools or database systems specially adapted for bioinformatics
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • G06N20/20Ensemble learning
    • GPHYSICS
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    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/044Recurrent networks, e.g. Hopfield networks
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    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/048Activation functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

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CN202111113164.1A 2018-10-15 2019-05-09 基于神经网络实现的方法和系统 Pending CN113705585A (zh)

Applications Claiming Priority (15)

Application Number Priority Date Filing Date Title
USPCT/US2018/055881 2018-10-15
US16/160,968 US11798650B2 (en) 2017-10-16 2018-10-15 Semi-supervised learning for training an ensemble of deep convolutional neural networks
PCT/US2018/055878 WO2019079180A1 (en) 2017-10-16 2018-10-15 NEURONAL NETWORKS WITH DEEP CONVOLUTION OF VARIANT CLASSIFICATION
US16/160,903 US10423861B2 (en) 2017-10-16 2018-10-15 Deep learning-based techniques for training deep convolutional neural networks
PCT/US2018/055840 WO2019079166A1 (en) 2017-10-16 2018-10-15 TECHNIQUES BASED ON DEEP LEARNING LEARNING OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
US16/160,986 US11315016B2 (en) 2017-10-16 2018-10-15 Deep convolutional neural networks for variant classification
US16/160903 2018-10-15
USPCT/US2018/055878 2018-10-15
PCT/US2018/055881 WO2019079182A1 (en) 2017-10-16 2018-10-15 SEMI-SUPERVISED APPRENTICESHIP FOR THE LEARNING OF A SET OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
US16/160986 2018-10-15
US16/160968 2018-10-15
USPCT/US2018/055840 2018-10-15
US16/407149 2019-05-08
US16/407,149 US10540591B2 (en) 2017-10-16 2019-05-08 Deep learning-based techniques for pre-training deep convolutional neural networks
CN201980003263.9A CN111328419B (zh) 2018-10-15 2019-05-09 基于神经网络实现的方法和系统

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CN (2) CN111328419B (he)
AU (2) AU2019272062B2 (he)
IL (2) IL282689B1 (he)
NZ (1) NZ759665A (he)
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KR102418073B1 (ko) * 2020-06-08 2022-07-06 고려대학교 산학협력단 인공지능 기반 비디오 투시 연하검사 자동화 분석 장치 및 방법
CN111830408B (zh) * 2020-06-23 2023-04-18 朗斯顿科技(北京)有限公司 一种基于边缘计算和深度学习的电机故障诊断系统及方法
CN112003735B (zh) * 2020-07-28 2021-11-09 四川大学 一种感知风险的深度学习驱动的极限传输容量调整方法
CN112183088B (zh) * 2020-09-28 2023-11-21 云知声智能科技股份有限公司 词语层级确定的方法、模型构建方法、装置及设备
KR102279056B1 (ko) * 2021-01-19 2021-07-19 주식회사 쓰리빌리언 지식전이를 이용한 유전자변이의 병원성 예측 시스템
CN113299345B (zh) * 2021-06-30 2024-05-07 中国人民解放军军事科学院军事医学研究院 病毒基因分类的方法、装置及电子设备
CN113539354B (zh) * 2021-07-19 2023-10-27 浙江理工大学 一种高效预测革兰氏阴性菌ⅲ型和ⅳ型效应蛋白的方法
CN113822342B (zh) * 2021-09-02 2023-05-30 湖北工业大学 一种安全图卷积网络的文献分类方法及系统
CN113836892B (zh) * 2021-09-08 2023-08-08 灵犀量子(北京)医疗科技有限公司 样本量数据提取方法、装置、电子设备及存储介质
CN113963746B (zh) * 2021-09-29 2023-09-19 西安交通大学 一种基于深度学习的基因组结构变异检测系统及方法
US20240087683A1 (en) * 2022-09-14 2024-03-14 Microsoft Technology Licensing, Llc Classification using a machine learning model trained with triplet loss
CN115662520B (zh) * 2022-10-27 2023-04-14 黑龙江金域医学检验实验室有限公司 Bcr/abl1融合基因的检测方法及相关设备
CN116153396A (zh) * 2023-04-21 2023-05-23 鲁东大学 一种基于迁移学习的非编码变异预测方法
CN117688785B (zh) * 2024-02-02 2024-04-16 东北大学 一种基于种植思想的全张量重力梯度数据反演方法

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IL271091A (he) 2020-04-30
CN111328419B (zh) 2021-10-19
IL282689B1 (he) 2024-10-01
AU2019272062A1 (en) 2020-04-30
JP7200294B2 (ja) 2023-01-06
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IL282689A (he) 2021-06-30
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AU2019272062B2 (en) 2021-08-19
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WO2020081122A1 (en) 2020-04-23
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